Data Drought in the Humid Tropics: How to Overcome the Cloud Barrier in Greenhouse Gas Remote Sensing
Abstract
Diagnosing land-atmosphere fluxes of carbon-dioxide (CO2) and methane (CH4) is essential for evaluating carbon-climate feedbacks. Greenhouse gas satellite missions aim to fill data gaps in regions like the humid tropics but obtain very few valid measurements due to cloud contamination. We examined data yields from the Orbiting Carbon Observatory alongside Sentinel-2 cloud statistics. We find that the main contribution to low data yields are frequent shallow cumulus clouds. In the Amazon, the success rate in obtaining valid measurements vary from 0.1% to 1.0%. By far the lowest yields occur in the wet season, consistent with Sentinel-2 cloud patterns. We find that increasing the spatial resolution of observations to ∼200 m would increase yields by 2–3 orders of magnitude and allow regular measurements in the wet season. Thus, the key to effective tropical greenhouse gas observations lies in regularly acquiring high-spatial resolution data.
Copyright and License
© 2024. The Authors. This is an open access article under theterms of the Creative CommonsA ttribution‐NonCommercial‐NoDerivs License, which permits use anddistribution in any medium, provided theoriginal work is properly cited, the use isnon‐commercial and no modifications oradaptations are made
Acknowledgement
Christian Frankenberg and Yinon M. Bar-On contributed equally to this work. We thank the Google team to not only provide the Google Earth Engine but also the CloudScore+ cloud product, which greatly facilitated our work. We acknowledge funding through the NASA OCO2/3 science team (Grant 80NSSC18K0895). Yinon M. Bar-On is a Schmidt Science Fellow.
Data Availability
All data sets used in this manuscript are publicly available and archived either through NASA data centers or Google Earth Engine. OCO-2 and OCO-3 XCO2 lite files are available from the DAAC archive (OCO-2/OCO-3 Science Team, Chatterjee, & Payne, 2022; OCO-2/OCO-3 Science Team, Payne, & Chatterjee, 2022). OCO-2 and OCO-3 L1b files are available from the same DAAC (OCO-2 Science Team et al., 2022). Cloud identification data based on Sentinel-2 are obtained through the Cloud Score+ product (Pasquarella et al., 2023), publicly available as Image collection at https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED.
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Additional details
- ISSN
- 1944-8007
- National Aeronautics and Space Administration
- 80NSSC18K0895
- California Institute of Technology
- Schmidt Science Fellow
- Schmidt Family Foundation
- Caltech groups
- Division of Geological and Planetary Sciences